Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing.

MOTIVATION: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. RESULTS: The novel approach Dr Insight implements a frame-breaking statistical model for the 'hand-shake' between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug-target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks. AVAILABILITY AND IMPLEMENTATION: Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phantom: investigating heterogeneous gene sets in time-course data.

MOTIVATION: Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes. RESULTS: We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time. AVAILABILITY AND IMPLEMENTATION: Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom . R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html . CONTACT: jinghua.gu@bswhealth.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function.

Patterns of gene expression in the central nervous system are highly variable and heritable. This genetic variation among normal individuals leads to considerable structural, functional and behavioral differences. We devised a general approach to dissect genetic networks systematically across biological scale, from base pairs to behavior, using a reference population of recombinant inbred strains. We profiled gene expression using Affymetrix oligonucleotide arrays in the BXD recombinant inbred strains, for which we have extensive SNP and haplotype data. We integrated a complementary database comprising 25 years of legacy phenotypic data on these strains. Covariance among gene expression and pharmacological and behavioral traits is often highly significant, corroborates known functional relations and is often generated by common quantitative trait loci. We found that a small number of major-effect quantitative trait loci jointly modulated large sets of transcripts and classical neural phenotypes in patterns specific to each tissue. We developed new analytic and graph theoretical approaches to study shared genetic modulation of networks of traits using gene sets involved in neural synapse function as an example. We built these tools into an open web resource called WebQTL that can be used to test a broad array of hypotheses.

mTORC1 links pathology in experimental models of Still's disease and macrophage activation syndrome.

Still's disease is a severe inflammatory syndrome characterized by fever, skin rash and arthritis affecting children and adults. Patients with Still's disease may also develop macrophage activation syndrome, a potentially fatal complication of immune dysregulation resulting in cytokine storm. Here we show that mTORC1 (mechanistic target of rapamycin complex 1) underpins the pathology of Still's disease and macrophage activation syndrome. Single-cell RNA sequencing in a murine model of Still's disease shows preferential activation of mTORC1 in monocytes; both mTOR inhibition and monocyte depletion attenuate disease severity. Transcriptomic data from patients with Still's disease suggest decreased expression of the mTORC1 inhibitors TSC1/TSC2 and an mTORC1 gene signature that strongly correlates with disease activity and treatment response. Unrestricted activation of mTORC1 by Tsc2 deletion in mice is sufficient to trigger a Still's disease-like syndrome, including both inflammatory arthritis and macrophage activation syndrome with hemophagocytosis, a cellular manifestation that is reproduced in human monocytes by CRISPR/Cas-mediated deletion of TSC2. Consistent with this observation, hemophagocytic histiocytes from patients with macrophage activation syndrome display prominent mTORC1 activity. Our study suggests a mechanistic link of mTORC1 to inflammation that connects the pathogenesis of Still's disease and macrophage activation syndrome.

Computational, integrative, and comparative methods for the elucidation of genetic coexpression networks.

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.

Two "wild-type" variants of Escherichia coli sigma(70): context-dependent effects of the identity of amino acid 149.

The identity of amino acid 149 of Escherichia coli sigma(70) has been reported variably as either arginine or aspartic acid. We show that the behavior of both a region 1.2 deletion and a single-amino-acid substitution at position 122 are greatly affected by the identity of amino acid 149.